ICCS is well known for its excellent line-up of keynote speakers.
This page will be frequently updated with new names, lecture titles and abstracts.
Maciej Besta, ETH Zürich, Switzerland
Marian Bubak, AGH University of Science and Technology, Poland | Sano Centre for Computational Medicine, Poland
Marco Viceconti, University of Bologna, Italy
Krzysztof Walczak, Poznan University of Economics and Business, Poland
Jessica Zhang, Carnegie Mellon University, USA
Material Transport Simulation in Complex Neurite Networks Using Isogeometric Analysis and Machine Learning Techniques
ETH Zürich, Switzerland
WEB
Maciej is a researcher from ETH Zurich. He works on large-scale graph computations and high-performance networking. He won, among others, the competition for the Best Student of Poland (2012), the first Google Fellowship in Parallel Computing (2013), and the ACM/IEEE-CS George Michael HPC Fellowship (2015). He received Best Paper awards and Best Student Paper awards at ACM/IEEE Supercomputing 2013, 2014, and 2019, at ACM HPDC 2015 and 2016, ACM Research Highlights 2018, and several more best paper nominations (ACM HPDC 2014, ACM FPGA 2019, and ACM/IEEE Supercomputing 2019). More detailed information on a personal site: https://people.inf.ethz.ch/bestam
Coming soon.
AGH University of Science and Technology, Poland | Sano Centre for Computational Medicine, Poland
WEB1 | WEB 2
Bio coming soon.
Coming soon.
University of Bologna, Italy
WEB
Marco Viceconti is full professor of Computational Biomechanics in the department of Industrial Engineering of the Alma Mater Studiorum – University of Bologna, and Director of the Medical Technology Lab of the Rizzoli Orthopaedic Institute. Prof Viceconti is an expert of neuromusculoskeletal biomechanics in general, and in particular in the use of subject-specific modelling to support the medical decision. He is one of 25 members of the World Council of Biomechanics. Prof Viceconti is one of the key figures in the in silico medicine international community. According to SCOPUS he published 351 papers (H-index = 50).
Coming soon.
Poznan University of Economics and Business, Poland
WEB
Krzysztof Walczak received the M.Sc. degree in Electronics and Telecommunications in 1992 and in Computer Science in 1994, both from the Technical University of Poznan. He received the Ph.D. degree in Computer Science in 2001 from the Technical University of Gdansk. In 2010 he received habilitation degree from the Technical University of Gdansk. From 1992 to 1996 he was with the Franco-Polish School of New Information and Communication Technologies in Poznan. He spent over one year as an invited researcher at the Syracuse University, NY, USA. In 1996 he joined the Department of Information Technology at the Poznan University of Economics, where currently he is an associate professor. His current research interests include virtual reality systems, multimedia systems, interactive television, Internet and intranet communication, and databases. He was acting as a technical coordinator in numerous research and industrial projects in these domains.
He has authored or co-authored over 100 research articles published in books, journals and proceedings of international conferences. He is also the author of several European and US patents.
He is a member of Executive Committee of EuroVR Association, ACM (Association for Computing Machinery), Web3D Consortium and Board of Directors of VSMM (International Society on Virtual Systems and Multimedia).
Coming soon.
Using Isogeometric Analysis and Machine Learning Techniques
Carnegie Mellon University, USA
WEB
Jessica Zhang is the George Tallman Ladd and Florence Barrett Ladd Professor of Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in Biomedical Engineering. She received her B.Eng. in Automotive Engineering, and M.Eng. in Engineering Mechanics from Tsinghua University, China; and M.Eng. in Aerospace Engineering and Engineering Mechanics and Ph.D. in Computational Engineering and Sciences from Institute for Computational Engineering and Sciences (now Oden Institute), The University of Texas at Austin. Her research interests include image processing, computational geometry, finite element method, isogeometric analysis, data-driven simulation and their applications in computational biomedicine, materials science and engineering. Zhang has co-authored over 190 publications in peer-reviewed journals and conference proceedings and received several Best Paper Awards. She published a book entitled “Geometric Modeling and Mesh Generation from Scanned Images” with CRC Press, Taylor & Francis Group in 2016. Zhang is the recipient of Simons Visiting Professorship from Mathematisches Forschungsinstitut Oberwolfach of Germany, US Presidential Early Career Award for Scientists and Engineers, NSF CAREER Award, Office of Naval Research Young Investigator Award, and USACM Gallagher Young Investigator Award. At CMU, she received David P. Casasent Outstanding Research Award, George Tallman Ladd and Florence Barrett Ladd Professorship, Clarence H. Adamson Career Faculty Fellow in Mechanical Engineering, Donald L. & Rhonda Struminger Faculty Fellow, and George Tallman Ladd Research Award. She is a Fellow of AIMBE, ASME, USACM and ELATE at Drexel.
Neurons exhibit remarkably complex geometry in their neurite networks. So far, how materials are transported in the complex geometry for survival and function of neurons remains an unanswered question. Answering this question is fundamental to understanding the physiology and disease of neurons. Here, we develop an isogeometric analysis (IGA) based platform for material transport simulation in neurite networks. We model the transport process by reaction-diffusion-transport equations and represent geometry of the networks using truncated hierarchical tricubic B-splines (THB-spline3D). We solve the Navier-Stokes equations to obtain the velocity field of material transport in the networks. We then solve the transport equations using the streamline upwind/Petrov-Galerkin (SU/PG) method. Using our IGA solver, we simulate material transport in a number of representative and complex neurite networks. From the simulation we discover several spatial patterns of the transport process. Together, our simulation provides key insights into how material transport in neurite networks is mediated by their complex geometry.
To enable fast prediction of the transport process within complex neurite networks, we develop a Graph Neural Networks (GNN) based model to learn the material transport mechanism from simulation data. In this study, we build the graph representation of the neuron by decomposing the neuron geometry into two basic structures: pipe and bifurcation. Different GNN simulators are designed for these two basic structures to predict the spatiotemporal concentration distribution given input simulation parameters and boundary conditions. In particular, we add the residual term from PDEs to instruct the model to learn the physics behind the simulation data. To recover the neurite network, a GNN-based assembly model is used to combine all the pipes and bifurcations following the graph representation. The loss function of the assembly model is designed to impose consistent concentration results on the interface between pipe and bifurcation. Through machine learning, we can quickly and accurately provide a prediction of material transport given a new complex neuron tree.